Almost 2,000 new substances appear every year and most of them have their biological and toxicological activity unknown. In order to address this concern, the aim of this research area focuses on the development of QSAR computational techniques that allow the prediction of such activities before they are studied by conventional techniques. In addition, some of our studies have developed both new parameters to facilitate the identification of the best model and several useful structural alerts for toxicological classification of new substances. In recent years interest has grown by these techniques due to the emergence of the European directive REACH. This Regulation states that “Information on intrinsic properties of substances may be generated by means other than in vivo tests” (e.g. from QSARs, etc.). This group has been involved in several projects related to the use of QSAR for the REACH registration of chemicals.


Some previous group results

Helguera, Aliuska Morales, Alfonso Pérez-Garrido, Alexandra Gaspar, Joana Reis, Fernando Cagide, Dolores Vina, M. Cordeiro, and Fernanda Borges. “Combining QSAR classification models for predictive modeling of human monoamine oxidase inhibitors.” European Journal of Medicinal Chemistry(2012).


Pérez-Garrido, Alfonso, Aliuska Morales Helguera, Francisco Girón Rodríguez, and M. N. D. S. Cordeiro. “QSAR models to predict mutagenicity of acrylates, methacrylates and,-unsaturated carbonyl compounds.” Dental Materials 26, no. 5 (2010): 397-415.


Pérez-Garrido, Alfonso, Aliuska Morales Helguera, Adela Abellán Guillén, M. N. D. S. Cordeiro, and Amalio Garrido Escudero. “Convenient QSAR model for predicting the complexation of structurally diverse compounds with β-cyclodextrins.” Bioorganic & Medicinal Chemistry 17, no. 2 (2009): 896-904.


Pérez-Garrido, Alfonso, Aliuska Morales Helguera, Fernanda Borges, M. Natália DS Cordeiro, Virginia Rivero, and Amalio Garrido Escudero. “Two new parameters based on distances in a receiver operating characteristic chart for the selection of classification models.” Journal of Chemical Information and Modeling 51, no. 10 (2011): 2746-2759.

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